Paper Title
Spam Detection and Spammer Behavior Analysis in Twitter using Reinforcement Learning

In today's sophisticated society, online social networking (OSN) sites like Twitter, Facebook, and LinkedIn are highly regarded. One of the most popular is Twitter, an OSN service. A considerable number of individuals use Twitter to communicate with one another. Twitter, the rapidly growing social network, has been inundated with spam. While individuals and companies use this data to gain a competitive advantage, spam or fraudulent users generate many data. Spam is estimated to account for one out of every 500 social media interactions and one out of every 25 tweets. This research presents a new approach that uses Reinforcement Learning for Twitter Spam Detection (TSD) and behavior analysis (RL) to find and eliminate spam in social media data. The article on social media goes into more depth on the conduct of spam Twitter users. Using this method, an ideal set of characteristics may be assembled without relying on tweets only accessible for a limited period on Twitter. Users' attributes are considered, and their Twitter accounts are verified via behavioral analysis. In experiments, we show that our approach is effective and resilient. We compare it to a typical feature set for SD in current methods, which offers a substantial increase in performance and accuracy. When used in conjunction with social media SD and user behavior analysis, this TSD technique is ideal for improving the quality of the material shared on the web in real-time. Keywords - TSD, Twitter, Spam, Spammer Behavior, OSN, RL